What do you do when your bosses’ boss asks you for an immediate ad hoc report on all the near-misses at your Houston plant, and you have half an hour to get it done and ensure it’s accurate because decisions are being made?
A) You confidently run a report, send it by email in a matter of minutes and you feel secure in the data you’re sending.
B) You quickly run a report, open Microsoft Excel, and cross your fingers that there aren’t too many records, so you can review them in advance for accuracy before you compile the report and send it.
C) You panic, look at all the papers on your desk, and immediately go home ill or ask for an extension.
If you answered A, your organization has put in place excellent data hygiene processes. Data hygiene refers to, “the collective processes conducted to ensure the cleanliness of data. Data is considered clean if it is relatively error-free.”
Companies can clean their data through manual processes and/or leveraging technology solutions. The most common areas for data inaccuracy are in fields that require human input. For example, imagine how much simpler your reporting would be if you had consistent formats for job titles? How would that improve your reporting?
In the example above, a single drop down would eliminate hours of manual data scrubbing.
Data hygiene is used to instill confidence in the data being collected and the decisions that are derived from this information. And according to the Harvard Business Review, if you answered A, you're part of the only 3% of company’s whose data meets basic quality standards. If you answered C, it’s time to source a technology solution to automate your workflows.
More than likely, your answer is B, and your organization’s performance is at the mercy of dirty data.
What is Dirty Data?
Dirty data refers to data which contains one or more of the following errors: it's inconsistently recorded, incomplete, contains duplicate records, or late. It has far-reaching consequences across your organization. Dirty data can compromise your business strategy, translate to missed opportunities, impact your company’s reputation, and increase your compliance risks. The costs of dirty data don’t stop either – they amplify and escalate as data is used across the organization. It gets further compounded as the volume of data being collected multiplies with those being generated by wearables, IoT devices, and more. Decisions based on dirty data can impact your ability to fund and secure additional resourcing for your health, safety, and environmental monitoring programs.
“Rather than launch a massive effort to clean up existing bad data, companies should focus on improving the way new data is created [thereby] identifying and eliminating the root causes of error.”
How to Clean-Up Your EHSQ Data
There are several ways to improve your data quality. Here are some you can implement immediately:
1. Completeness: Ensure your data records have all their required fields completed before submitting them. Identify what matters to everyone and ensure they fill out the mandatory fields and remove useless (or nice to have inputs). Ensure your forms are laid out in the order someone would expect to complete them. Use mouse-overs and guided text to assist users in completing them.
2. Conformity and Consistency of Forms: Just think of how many ways you can write your phone number down! Wherever possible, leverage a drop-down list or use technology and on-screen text to describe how data needs to be entered. Drop-down lists trump free-form text fields where applicable.
3. Audit Your Inputs: Standard operating procedures include investigating duplicate entries and looking for patterns into record type and record creator. Set up reports and pin them to dashboards (for example, all new patients that have been created today) to complete a 24-hour review of records and correct duplicate or incomplete entries immediately.
4. Leverage Technology: EHSQ vendors can provide technology to assist in the overall trust-worthiness of reporting and EHSQ record management. Look for a vendor with a Data Quality Scoring tool to assist in monitoring and managing record timeliness and completeness.
Download our guide for more ideas on how you can improve your EHSQ data quality:
As your organization taps into the wealth of information contained within it, understand that data quality is a universal problem across all departments and organizations. As an EHSQ professional, you strive to do your best. By taking the opportunity to ensure the information you have is of highest quality, you’re taking the lead in ensuring your area is functioning to thrive.
About the AuthorMore Content by Jessica Shields